# ==================================================================== # # TITLE # # Antimicrobial Resistance (AMR) Analysis for R # # # # SOURCE # # https://github.com/msberends/AMR # # # # LICENCE # # (c) 2018-2020 Berends MS, Luz CF et al. # # Developed at the University of Groningen, the Netherlands, in # # collaboration with non-profit organisations Certe Medical # # Diagnostics & Advice, and University Medical Center Groningen. # # # # This R package is free software; you can freely use and distribute # # it for both personal and commercial purposes under the terms of the # # GNU General Public License version 2.0 (GNU GPL-2), as published by # # the Free Software Foundation. # # We created this package for both routine data analysis and academic # # research and it was publicly released in the hope that it will be # # useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY. # # # # Visit our website for the full manual and a complete tutorial about # # how to conduct AMR analysis: https://msberends.github.io/AMR/ # # ==================================================================== # #' Guess antibiotic column #' #' This tries to find a column name in a data set based on information from the [antibiotics] data set. Also supports WHONET abbreviations. #' @inheritSection lifecycle Stable lifecycle #' @param x a [data.frame] #' @param search_string a text to search `x` for, will be checked with [as.ab()] if this value is not a column in `x` #' @param verbose a logical to indicate whether additional info should be printed #' @details You can look for an antibiotic (trade) name or abbreviation and it will search `x` and the [antibiotics] data set for any column containing a name or code of that antibiotic. **Longer columns names take precedence over shorter column names.** #' @return A column name of `x`, or `NULL` when no result is found. #' @export #' @inheritSection AMR Read more on our website! #' @examples #' df <- data.frame(amox = "S", #' tetr = "R") #' #' guess_ab_col(df, "amoxicillin") #' # [1] "amox" #' guess_ab_col(df, "J01AA07") # ATC code of tetracycline #' # [1] "tetr" #' #' guess_ab_col(df, "J01AA07", verbose = TRUE) #' # NOTE: Using column `tetr` as input for `J01AA07` (tetracycline). #' # [1] "tetr" #' #' # WHONET codes #' df <- data.frame(AMP_ND10 = "R", #' AMC_ED20 = "S") #' guess_ab_col(df, "ampicillin") #' # [1] "AMP_ND10" #' guess_ab_col(df, "J01CR02") #' # [1] "AMC_ED20" #' guess_ab_col(df, as.ab("augmentin")) #' # [1] "AMC_ED20" #' #' # Longer names take precendence: #' df <- data.frame(AMP_ED2 = "S", #' AMP_ED20 = "S") #' guess_ab_col(df, "ampicillin") #' # [1] "AMP_ED20" guess_ab_col <- function(x = NULL, search_string = NULL, verbose = FALSE) { meet_criteria(x, allow_class = "data.frame", allow_NULL = TRUE) meet_criteria(search_string, allow_class = "character", has_length = 1, allow_NULL = TRUE) meet_criteria(verbose, allow_class = "logical", has_length = 1) if (is.null(x) & is.null(search_string)) { return(as.name("guess_ab_col")) } if (search_string %in% colnames(x)) { ab_result <- search_string } else { search_string.ab <- suppressWarnings(as.ab(search_string)) if (search_string.ab %in% colnames(x)) { ab_result <- colnames(x)[colnames(x) == search_string.ab][1L] } else if (any(tolower(colnames(x)) %in% tolower(unlist(ab_property(search_string.ab, "abbreviations", language = NULL))))) { ab_result <- colnames(x)[tolower(colnames(x)) %in% tolower(unlist(ab_property(search_string.ab, "abbreviations", language = NULL)))][1L] } else { # sort colnames on length - longest first cols <- colnames(x[, x %pm>% colnames() %pm>% nchar() %pm>% order() %pm>% rev()]) df_trans <- data.frame(cols = cols, abs = suppressWarnings(as.ab(cols)), stringsAsFactors = FALSE) ab_result <- df_trans[which(df_trans$abs == search_string.ab), "cols"] ab_result <- ab_result[!is.na(ab_result)][1L] } } if (length(ab_result) == 0) { if (verbose == TRUE) { message(paste0("No column found as input for `", search_string, "` (", ab_name(search_string, language = NULL, tolower = TRUE), ").")) } return(NULL) } else { if (verbose == TRUE) { message(font_blue(paste0("NOTE: Using column `", font_bold(ab_result), "` as input for `", search_string, "` (", ab_name(search_string, language = NULL, tolower = TRUE), ")."))) } return(ab_result) } } get_column_abx <- function(x, soft_dependencies = NULL, hard_dependencies = NULL, verbose = FALSE, info = TRUE, ...) { meet_criteria(x, allow_class = "data.frame") meet_criteria(soft_dependencies, allow_class = "character", allow_NULL = TRUE) meet_criteria(hard_dependencies, allow_class = "character", allow_NULL = TRUE) meet_criteria(verbose, allow_class = "logical", has_length = 1) meet_criteria(info, allow_class = "logical", has_length = 1) if (info == TRUE) { message(font_blue("NOTE: Auto-guessing columns suitable for analysis"), appendLF = FALSE) } x <- as.data.frame(x, stringsAsFactors = FALSE) if (NROW(x) > 10000) { # only test maximum of 10,000 values per column if (info == TRUE) { message(font_blue(paste0(" (using only ", font_bold("the first 10,000 rows"), ")...")), appendLF = FALSE) } x <- x[1:10000, , drop = FALSE] } else if (info == TRUE) { message(font_blue("..."), appendLF = FALSE) } x_bak <- x # only check columns that are a valid AB code, ATC code, name, abbreviation or synonym, # or already have the rsi class (as.rsi) # and that have no more than 50% invalid values vectr_antibiotics <- unique(toupper(unlist(antibiotics[, c("ab", "atc", "name", "abbreviations", "synonyms")]))) vectr_antibiotics <- vectr_antibiotics[!is.na(vectr_antibiotics) & nchar(vectr_antibiotics) >= 3] x_columns <- sapply(colnames(x), function(col, df = x_bak) { if (toupper(col) %in% vectr_antibiotics | is.rsi(as.data.frame(df)[, col, drop = TRUE]) | is.rsi.eligible(as.data.frame(df)[, col, drop = TRUE], threshold = 0.5)) { return(col) } else { return(NA_character_) } }) x_columns <- x_columns[!is.na(x_columns)] x <- x[, x_columns, drop = FALSE] # without drop = TRUE, x will become a vector when x_columns is length 1 df_trans <- data.frame(colnames = colnames(x), abcode = suppressWarnings(as.ab(colnames(x), info = FALSE))) df_trans <- df_trans[!is.na(df_trans$abcode), , drop = FALSE] x <- as.character(df_trans$colnames) names(x) <- df_trans$abcode # add from self-defined dots (...): # such as get_column_abx(example_isolates %pm>% rename(thisone = AMX), amox = "thisone") dots <- list(...) if (length(dots) > 0) { newnames <- suppressWarnings(as.ab(names(dots), info = FALSE)) if (any(is.na(newnames))) { warning("Invalid antibiotic reference(s): ", toString(names(dots)[is.na(newnames)]), call. = FALSE, immediate. = TRUE) } # turn all NULLs to NAs dots <- unlist(lapply(dots, function(x) if (is.null(x)) NA else x)) names(dots) <- newnames dots <- dots[!is.na(names(dots))] # merge, but overwrite automatically determined ones by 'dots' x <- c(x[!x %in% dots & !names(x) %in% names(dots)], dots) # delete NAs, this will make e.g. eucast_rules(... TMP = NULL) work to prevent TMP from being used x <- x[!is.na(x)] } if (length(x) == 0) { if (info == TRUE) { message(font_blue("No columns found.")) } return(x) } # sort on name x <- x[order(names(x), x)] duplicates <- c(x[duplicated(x)], x[duplicated(names(x))]) duplicates <- duplicates[unique(names(duplicates))] x <- c(x[!names(x) %in% names(duplicates)], duplicates) x <- x[order(names(x), x)] # succeeded with auto-guessing if (info == TRUE) { message(font_blue("OK.")) } for (i in seq_len(length(x))) { if (info == TRUE & verbose == TRUE & !names(x[i]) %in% names(duplicates)) { message(font_blue(paste0("NOTE: Using column `", font_bold(x[i]), "` as input for `", names(x)[i], "` (", ab_name(names(x)[i], tolower = TRUE, language = NULL), ")."))) } if (info == TRUE & names(x[i]) %in% names(duplicates)) { warning(font_red(paste0("Using column `", font_bold(x[i]), "` as input for `", names(x)[i], "` (", ab_name(names(x)[i], tolower = TRUE, language = NULL), "), although it was matched for multiple antibiotics or columns.")), call. = FALSE, immediate. = verbose) } } if (!is.null(hard_dependencies)) { hard_dependencies <- unique(hard_dependencies) if (!all(hard_dependencies %in% names(x))) { # missing a hard dependency will return NA and consequently the data will not be analysed missing <- hard_dependencies[!hard_dependencies %in% names(x)] generate_warning_abs_missing(missing, any = FALSE) return(NA) } } if (!is.null(soft_dependencies)) { soft_dependencies <- unique(soft_dependencies) if (info == TRUE & !all(soft_dependencies %in% names(x))) { # missing a soft dependency may lower the reliability missing <- soft_dependencies[!soft_dependencies %in% names(x)] missing_msg <- paste(paste0(ab_name(missing, tolower = TRUE, language = NULL), " (", missing, ")"), collapse = ", ") missing_msg <- paste("NOTE: Reliability would be improved if these antimicrobial results would be available too:", missing_msg) wrapped <- strwrap(missing_msg, width = 0.95 * getOption("width"), exdent = 6) wrapped <- gsub("\\((.*?)\\)", paste0("(", font_bold("\\1"), ")"), wrapped) # add bold abbreviations message(font_blue(wrapped, collapse = "\n")) } } x } generate_warning_abs_missing <- function(missing, any = FALSE) { missing <- paste0(missing, " (", ab_name(missing, tolower = TRUE, language = NULL), ")") if (any == TRUE) { any_txt <- c(" any of", "is") } else { any_txt <- c("", "are") } warning(paste0("Introducing NAs since", any_txt[1], " these antimicrobials ", any_txt[2], " required: ", paste(missing, collapse = ", ")), immediate. = TRUE, call. = FALSE) }